In order to improve the accuracy and still keep a low complexity of the code several improvements to the presented work can be done.
	
	Similar to GraphSlam's post processing step, the addition of a scan matching algorithm would help in incorporating the motion steps by comparing the sensor results of two consecutive scans against the expected motion, thus getting a good estimate of the odometry error. This could be further improved by varying the weights of the range sensors and the odometry sensors depending on the number of landmarks that can be detected and on the motion performed.
	In addition, when the robot enters an already visited area after a while, searching for matches between the current scans' landmarks and previous landmarks and linking these together will yield cycles or loop closing and therefore it will be able to correct poses backwards in time.
	
	The exploration can also be improved by comparing the expected information gain of visiting a certain point with the actual gain resulted after visiting it, and using this information to improve the different weights used in the algorithm.
	
	In order to reduce the required computing time, lowering the messaging overhead between the various components can be achieved by reducing the number of ROS nodes used, as well as optimizing different parts of the code.
	
	TinySlam proves to be a good base for adding these improvements and extensions because while being robust, its low complexity and clean structure make it easy to understand and modify.
	
	An even more robust scan filtering would enhance the accuracy af the mapping of tiny slam. Especially filtering noise that yields unoccupied areas behind walls would increase the performance of the frontier detection algorithm. 